Tools: Best Hardware for OpenClaw in 2026 — Mac Mini vs Jetson vs Raspberry Pi

Why Hardware Matters for OpenClaw ## Option 1: Raspberry Pi 5 (8GB) — ~€95 ## Option 2: Mac Mini M4 — ~€650+ ## Option 3: Generic x86 Mini PCs — €150-400 ## Option 4: NVIDIA Jetson Orin Nano (ClawBox) — €399 ## The Comparison Table ## My Recommendation ## Final Thoughts If you've decided to run OpenClaw as your self-hosted AI assistant, the next question is obvious: what hardware should you run it on? I spent the last few months testing OpenClaw on everything from a Raspberry Pi 5 to a Mac Mini M4. Here's what I learned about the best hardware for OpenClaw — and why there's no single right answer. OpenClaw isn't just a chatbot. It orchestrates browser automation, manages multiple messaging channels (Telegram, WhatsApp, Discord), runs local LLM inference or proxies to cloud APIs, and handles real-time tool calls. That means your hardware needs to: Let's look at the openclaw hardware requirements across four popular options. The Pi 5 is the cheapest entry point. With its quad-core Cortex-A76 and 8GB RAM, it can technically run OpenClaw's core services. Verdict: Good for experimenting. Not great for daily-driving OpenClaw with browser automation and multiple channels. If you're only proxying to cloud APIs (OpenAI, Anthropic) and running light workloads, it works — but you'll feel the limits. For a deeper comparison, check out the Raspberry Pi vs Jetson breakdown. The M4 Mac Mini is a beast. Apple Silicon's unified memory architecture, hardware media engine, and single-thread performance make it arguably the best consumer hardware for running AI workloads. Verdict: If budget isn't a concern and you want to run 7B-13B parameter models locally, the Mac Mini M4 is hard to beat. But for many OpenClaw users, it's more machine (and more money) than necessary. Looking for a more affordable path? See the Mac Mini alternative guide. The N100/N305 mini PCs flooding Amazon and AliExpress are surprisingly capable. You get an x86 platform with 16GB RAM, NVMe storage, and decent I/O. Verdict: A solid middle ground if you want standard Linux compatibility and don't care about on-device AI inference. Pick a fanless model with 16GB RAM and NVMe, and you'll have a reliable OpenClaw host. This is what I personally run. The ClawBox is an NVIDIA Jetson Orin Nano packaged with a 512GB NVMe SSD and OpenClaw pre-installed. Verdict: The sweet spot if you want local AI inference without Mac Mini prices. The 67 TOPS of dedicated AI compute means you can actually run quantized models on-device, and 15W means your electricity bill won't notice. The pre-installed OpenClaw setup means you're literally up and running in minutes. Here's how I'd break it down: Choose the Raspberry Pi 5 if you're experimenting, learning, or only using cloud AI APIs. Budget-friendly and fun to tinker with. Choose a Mini PC if you want standard x86 Linux, have existing infrastructure, and don't need local AI inference. Choose the ClawBox if you want a dedicated, silent, always-on AI assistant with actual GPU acceleration at a reasonable price. It's the device I reach for when people ask me "what should I buy to run OpenClaw?" Choose the Mac Mini M4 if budget isn't an issue, you want the most powerful local inference, and you're comfortable with macOS. For a full breakdown of what OpenClaw needs to run smoothly, check the hardware requirements page. There's no single "best hardware for OpenClaw" — it depends on your budget, your use case, and whether you want local AI inference. What I will say is: don't overthink it. OpenClaw runs on anything from a Pi to a workstation. Pick what fits your life, plug it in, and start building your AI assistant. The hardware is the easy part. The fun part is what you do with it. Have questions about hardware compatibility? Drop a comment below or check openclawhardware.dev for detailed specs and benchmarks. 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Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - Stay on 24/7 (it's an assistant, not an app you open) - Handle concurrent I/O without choking - Optionally run local AI models for privacy - Be quiet and power-efficient enough for a desk or shelf - Huge community, tons of accessories - Low power (~5-10W) - No GPU acceleration — forget local LLM inference - SD card I/O bottleneck (NVMe HAT helps, but adds cost) - 8GB RAM is tight once you add Node.js, browser automation, and a database - Thermal throttling under sustained load - Incredible single-thread performance - 16GB+ unified memory — great for local models - macOS ecosystem, polished experience - Quiet, compact, beautiful design - Price — €650 for the base model, and you probably want 24GB RAM (€880+) - macOS quirks with headless operation and automation - Overkill if you're not running large local models - Not designed for 24/7 embedded/server use - Good price-to-performance ratio - Standard Linux support - Enough RAM for OpenClaw + light local models (quantized) - Many options at every price point - No dedicated AI accelerator - CPU-only inference is slow for anything meaningful - Build quality varies wildly - Fan noise on cheaper models - 67 TOPS of AI compute — run local models with actual GPU acceleration - 15W power consumption, completely fanless - OpenClaw pre-installed and pre-configured - Compact, silent, runs 24/7 without thinking about it - CUDA ecosystem for future AI workloads - ARM64 — some x86 software won't run (though most server stuff works fine) - 8GB unified RAM shared between CPU and GPU - NVIDIA's JetPack ecosystem has a learning curve - Less community support than Raspberry Pi or x86